OpenRaiser/Intern-Atlas huggingface dataset:
What Makes Effective Leadership in Agile Software Development Teams?
Venue: arXiv cs.SE 2022
Year: 2022

Effective leadership is one of the key drivers of business and project success, and one of the most active areas of management research. But how does leadership work in agile software development, which emphasizes self-management and self-organization and marginalizes traditional leadership roles? To find out, this study examines agile leadership from the perspective of thirteen professionals who identify as agile leaders, in different roles, at ten different software development companies of varying sizes. Data from semi-structured interviews reveals that leadership: (1) is dynamically shared among team members; (2) engenders a sense of belonging to the team; and (3) involves balancing competing organizational cultures (e.g. balancing the new agile culture with the old milestone-driven culture). In other words, agile leadership is a property of a team, not a role, and effectiveness depends on agile team members' identifying with the team, accepting responsibility, and being sensitive to cultural conflict.
Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation
Venue: arXiv cs.CL 2022
Year: 2022

In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involving English. JASS focuses on masking and reordering Japanese linguistic units known as bunsetsu, whereas ENSS is proposed based on phrase structure masking and reordering tasks. Experiments on ASPEC Japanese--English & Japanese--Chinese, Wikipedia Japanese--Chinese, News English--Korean corpora demonstrate that JASS and ENSS outperform MASS and other existing language-agnostic pre-training methods by up to +2.9 BLEU points for the Japanese--English tasks, up to +7.0 BLEU points for the Japanese--Chinese tasks and up to +1.3 BLEU points for English--Korean tasks. Empirical analysis, which focuses on the relationship between individual parts in JASS and ENSS, reveals the complementary nature of the subtasks of JASS and ENSS. Adequacy evaluation using LASER, human evaluation, and case studies reveals that our proposed methods significantly outperform pre-training methods without injected linguistic knowledge and they have a larger positive impact on the adequacy as compared to the fluency. We release codes here: https://github.com/Mao-KU/JASS/tree/master/linguistically-driven-pretraining.
JEDI: These aren't the JSON documents you're looking for... (Extended Version*)
Venue: arXiv cs.DB 2022
Year: 2022

The JavaScript Object Notation (JSON) is a popular data format used in document stores to natively support semi-structured data. In this paper, we address the problem of JSON similarity lookup queries: given a query document and a distance threshold $τ$, retrieve all JSON documents that are within $τ$ from the query document. Due to its recursive definition, JSON data are naturally represented as trees. Different from other hierarchical formats such as XML, JSON supports both ordered and unordered sibling collections within a single document. This feature poses a new challenge to the tree model and distance computation. We propose JSON tree, a lossless tree representation of JSON documents, and define the JSON Edit Distance (JEDI), the first edit-based distance measure for JSON documents. We develop an algorithm, called QuickJEDI, for computing JEDI by leveraging a new technique to prune expensive sibling matchings. It outperforms a baseline algorithm by an order of magnitude in runtime. To boost the performance of JSON similarity queries, we introduce an index called JSIM and a highly effective upper bound based on tree sorting. Our algorithm for the upper bound runs in $O(n τ)$ time and $O(n + τ\log n)$ space, which substantially improves the previous best bound of $O(n^2)$ time and $O(n \log n)$ space (where $n$ is the tree size). Our experimental evaluation shows that our solution scales to databases with millions of documents and JSON trees with tens of thousands of nodes.
Reversible Data Hiding in Encrypted Images by Lossless Pixel Conversion
Venue: arXiv cs.CR 2022
Year: 2022

Reversible data hiding in encrypted image (RDHEI) becomes a hot topic, and a lot of algorithms have been proposed to optimize this technology. However, these algorithms cannot achieve strong embedding capacity. Thus, in this paper, we propose an advanced RDHEI scheme based on lossless pixel conversion (LPC). Different from the previous RDHEI algorithms, LPC is inspired by the planar map coloring question, and it performs a dynamic image division process to divide the original image into irregular regions instead of regular blocks as in the previous RDHEI algorithms. In the process of LPC, pixel conversion is performed by region; that is, pixels in the same regions are converted to the same conversion values, which will occupy a smaller size, and then the available room can be reserved to accommodate additional data. LPC is a reversible process, so the original image can be losslessly recovered on the receiver side. Experimental results show that the embedding capacity of the proposed scheme outperforms the existing RDHEI algorithms.
GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding
Venue: arXiv cs.CV 2022
Year: 2022

Reference-guided image inpainting restores image pixels by leveraging the content from another single reference image. The primary challenge is how to precisely place the pixels from the reference image into the hole region. Therefore, understanding the 3D geometry that relates pixels between two views is a crucial step towards building a better model. Given the complexity of handling various types of reference images, we focus on the scenario where the images are captured by freely moving the same camera around. Compared to the previous work, we propose a principled approach that does not make heuristic assumptions about the planarity of the scene. We leverage a monocular depth estimate and predict relative pose between cameras, then align the reference image to the target by a differentiable 3D reprojection and a joint optimization of relative pose and depth map scale and offset. Our approach achieves state-of-the-art performance on both RealEstate10K and MannequinChallenge dataset with large baselines, complex geometry and extreme camera motions. We experimentally verify our approach is also better at handling large holes.
Energy Efficiency Maximization in Large-Scale Cell-Free Massive MIMO: A Projected Gradient Approach
Venue: arXiv cs.IT 2022
Year: 2022

This paper considers the fundamental power allocation problem in cell-free massive mutiple-input and multiple-output (MIMO) systems which aims at maximizing the total energy efficiency (EE) under a sum power constraint at each access point (AP) and a quality-of-service (QoS) constraint at each user. Existing solutions for this optimization problem are based on solving a sequence of second-order cone programs (SOCPs), whose computational complexity scales dramatically with the network size. Therefore, they are not implementable for practical large-scale cell-free massive MIMO systems. To tackle this issue, we propose an iterative power control algorithm based on the frame work of an accelerated projected gradient (APG) method. In particular, each iteration of the proposed method is done by simple closed-form expressions, where a penalty method is applied to bring constraints into the objective in the form of penalty functions. Finally, the convergence of the proposed algorithm is analytically proved and numerically compared to the known solution based on SOCP. Simulations results demonstrate that our proposed power control algorithm can achieve the same EE as the existing SOCPs-based method, but more importantly, its run time is much lower (one to two orders of magnitude reduction in run time, compared to the SOCPs-based approaches).
Category-Association Based Similarity Matching for Novel Object Pick-and-Place Task
Venue: arXiv cs.RO 2022
Year: 2022

Robotic pick-and-place has been researched for a long time to cope with uncertainty of novel objects and changeable environments. Past works mainly focus on learning-based methods to achieve high precision. However, they have difficulty being generalized for the limitation of specified training models. To break through this drawback of learning-based approaches, we introduce a new perspective of similarity matching between novel objects and a known database based on category-association to achieve pick-and-place tasks with high accuracy and stabilization. We calculate the category name similarity using word embedding to quantify the semantic similarity between the categories of known models and the target real-world objects. With a similar model identified by a similarity prediction function, we preplan a series of robust grasps and imitate them to plan new grasps on the real-world target object. We also propose a distance-based method to infer the in-hand posture of objects and adjust small rotations to achieve stable placements under uncertainty. Through a real-world robotic pick-and-place experiment with a dozen of in-category and out-of-category novel objects, our method achieved an average success rate of 90.6% and 75.9% respectively, validating the capacity of generalization to diverse objects.
Active Connectivity Fundamentals for TSCH Networks of Mobile Robots
Venue: arXiv cs.NI 2022
Year: 2022

Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard which have been proven to be one of the most reliable options when it comes to industrial applications. TSCH has been designed to be utilized in static network topologies. Thus, if an application scenario requires a mobile network topology, TSCH does not perform reliably. In this paper we introduce active connectivity for mobile application scenarios, such as mobile robots. This is a feature that enables the option to regulate physical characteristics such as the speed of a node as it moves, in order to keep being connected to the TSCH network. We model the active connectivity approach through a basic example where two nodes are moving towards the same direction to infer the main principles of the introduced approach. We evaluate the active connectivity feature through simulations and quantify trade-off between connectivity and application-layer performance.
Exact learning for infinite families of concepts
Venue: arXiv cs.AI 2022
Year: 2022

In this paper, based on results of exact learning, test theory, and rough set theory, we study arbitrary infinite families of concepts each of which consists of an infinite set of elements and an infinite set of subsets of this set called concepts. We consider the notion of a problem over a family of concepts that is described by a finite number of elements: for a given concept, we should recognize which of the elements under consideration belong to this concept. As algorithms for problem solving, we consider decision trees of five types: (i) using membership queries, (ii) using equivalence queries, (iii) using both membership and equivalence queries, (iv) using proper equivalence queries, and (v) using both membership and proper equivalence queries. As time complexity, we study the depth of decision trees. In the worst case, with the growth of the number of elements in the problem description, the minimum depth of decision trees of the first type either grows as a logarithm or linearly, and the minimum depth of decision trees of each of the other types either is bounded from above by a constant or grows as a logarithm, or linearly. The obtained results allow us to distinguish seven complexity classes of infinite families of concepts.
"Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint
Venue: arXiv cs.SE 2022
Year: 2022

Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes, it is only a small piece of the software quality puzzle, especially in the case of ML projects given their additional challenges and lower degree of Software Engineering (SE) experience in the data scientists that develop them. We introduce the novel concept of project smells which consider deficits in project management as a more holistic perspective on software quality in ML projects. An open-source static analysis tool mllint was also implemented to help detect and mitigate these. Our research evaluates this novel concept of project smells in the industrial context of ING, a global bank and large software- and data-intensive organisation. We also investigate the perceived importance of these project smells for proof-of-concept versus production-ready ML projects, as well as the perceived obstructions and benefits to using static analysis tools such as mllint. Our findings indicate a need for context-aware static analysis tools, that fit the needs of the project at its current stage of development, while requiring minimal configuration effort from the user.
Minimax Demographic Group Fairness in Federated Learning
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness in federated learning scenarios where different participating entities may only have access to a subset of the population groups during the training phase. We formally analyze how our proposed group fairness objective differs from existing federated learning fairness criteria that impose similar performance across participants instead of demographic groups. We provide an optimization algorithm -- FedMinMax -- for solving the proposed problem that provably enjoys the performance guarantees of centralized learning algorithms. We experimentally compare the proposed approach against other state-of-the-art methods in terms of group fairness in various federated learning setups, showing that our approach exhibits competitive or superior performance.
An Information Theory Approach to Network Evolution Models
Venue: arXiv cs.IT 2022
Year: 2022

A novel Markovian network evolution model is introduced and analysed by means of information theory. It will be proved that the model, called Network Evolution Chain, is a stationary and ergodic stochastic process. Therefore, the Asymptotic Equipartition Property can be applied to it. The model's entropy rate and typical sequences are also explored. Extracting particular information from the network and methods to simulate network evolution in the continuous time domain are discussed. Additionally, the Erdos-Renyi Network Evolution Chain is introduced as a subset of our model with the additional property of its stationary distribution matching the Erdos-Renyi random graph model. The stationary distributions of nodes and graphs are calculated for this subset alongside its entropy rate. The simulation results at the end of the paper back up the proved theorems and calculated values.
Adaptive neighborhood Metric learning
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining. Since the inseparable samples are often mixed with hard samples, current informative sample mining strategies used to deal with inseparable problem may bring up some side-effects, such as instability of objective function, etc. To alleviate this problem, we propose a novel distance metric learning algorithm, named adaptive neighborhood metric learning (ANML). In ANML, we design two thresholds to adaptively identify the inseparable similar and dissimilar samples in the training procedure, thus inseparable sample removing and metric parameter learning are implemented in the same procedure. Due to the non-continuity of the proposed ANML, we develop an ingenious function, named \emph{log-exp mean function} to construct a continuous formulation to surrogate it, which can be efficiently solved by the gradient descent method. Similar to Triplet loss, ANML can be used to learn both the linear and deep embeddings. By analyzing the proposed method, we find it has some interesting properties. For example, when ANML is used to learn the linear embedding, current famous metric learning algorithms such as the large margin nearest neighbor (LMNN) and neighbourhood components analysis (NCA) are the special cases of the proposed ANML by setting the parameters different values. When it is used to learn deep features, the state-of-the-art deep metric learning algorithms such as Triplet loss, Lifted structure loss, and Multi-similarity loss become the special cases of ANML. Furthermore, the \emph{log-exp mean function} proposed in our method gives a new perspective to review the deep metric learning methods such as Prox-NCA and N-pairs loss. At last, promising experimental results demonstrate the effectiveness of the proposed method.
Body Models in Humans and Robots
Venue: arXiv cs.RO 2022
Year: 2022

Neurocognitive models of higher-level somatosensory processing have emphasised the role of stored body representations in interpreting real-time sensory signals coming from the body (Longo, Azanon and Haggard, 2010; Tame, Azanon and Longo, 2019). The need for such stored representations arises from the fact that immediate sensory signals coming from the body do not specify metric details about body size and shape. Several aspects of somatoperception, therefore, require that immediate sensory signals be combined with stored body representations. This basic problem is equally true for humanoid robots and, intriguingly, neurocognitive models developed to explain human perception are strikingly similar to those developed independently for localizing touch on humanoid robots, such as the iCub, equipped with artificial electronic skin on the majority of its body surface (Roncone et al., 2014; Hoffmann, 2021). In this chapter, we will review the key features of these models, discuss their similarities and differences to each other, and to other models in the literature. Using robots as embodied computational models is an example of synthetic methodology or 'understanding by building' (e.g., Hoffmann and Pfeifer, 2018), computational embodied neuroscience (Caligiore et al., 2010) or 'synthetic psychology of the self' (Prescott and Camilleri, 2019). Such models have the advantage that they need to be worked out into every detail, making any theory explicit and complete. There is also an additional way of (pre)validating such a theory other than comparing to the biological or psychological phenomenon studied by simply verifying that a particular implementation really performs the task: can the robot localize where it is being touched (see https://youtu.be/pfse424t5mQ)?
Influences of social media usage on public attitudes and behavior towards COVID-19 vaccine in the Arab world
Venue: arXiv cs.SI 2022
Year: 2022

Background: Vaccination programs are effective only when a significant percentage of people are vaccinated. However, vaccine acceptance varies among communities around the world. Social media usage is arguably one of the factors affecting public attitudes towards vaccines. Objective: This study aims to identify if the social media usages factors can be used to predict attitudes and behavior towards the COVID-19 vaccines among the people in the Arab world. Methods: An online survey was conducted in the Arab countries and 217 Arab people participated in this study. Logistic regression was applied to identify what demographics and social media usage factors predict public attitudes and behavior towards the COVID-19 vaccines. Results: Of the 217 participants, 56.22% of them were willing to accept the vaccine and 41.47% of them were hesitant. This study shows that none of the social media usages factors were significant enough to predict the actual vaccine acceptance behavior. Whereas the analysis showed few of the social media usage factors can predict public attitudes towards the COVID-19 vaccines. For example, frequent social media users were 2.85 times more likely to agree that the risk of COVID-19 is being exaggerated (OR=2.85, 95% CI=0.86-9.45, p=0.046) than infrequent social media users. Whereas participants having more trust in vaccine information shared by their contacts are less likely to agree that decision-makers have verified that vaccines are safe (OR=0.528, 95% CI= 0.276-1.012, p=0.05). Conclusion: The use of social media and information shared on it may affect public attitudes towards COVID-19 vaccines. Therefore, disseminating correct and validated information about COVID-19 and other vaccines on social media is important for increasing public trust and countering the impact of incorrect and misinformation.
AI Technical Considerations: Data Storage, Cloud usage and AI Pipeline
Venue: arXiv cs.AI 2022
Year: 2022

Artificial intelligence (AI), especially deep learning, requires vast amounts of data for training, testing, and validation. Collecting these data and the corresponding annotations requires the implementation of imaging biobanks that provide access to these data in a standardized way. This requires careful design and implementation based on the current standards and guidelines and complying with the current legal restrictions. However, the realization of proper imaging data collections is not sufficient to train, validate and deploy AI as resource demands are high and require a careful hybrid implementation of AI pipelines both on-premise and in the cloud. This chapter aims to help the reader when technical considerations have to be made about the AI environment by providing a technical background of different concepts and implementation aspects involved in data storage, cloud usage, and AI pipelines.
Unicorn: Reasoning about Configurable System Performance through the lens of Causality
Venue: arXiv cs.LG 2022
Year: 2022

Modern computer systems are highly configurable, with the total variability space sometimes larger than the number of atoms in the universe. Understanding and reasoning about the performance behavior of highly configurable systems, over a vast and variable space, is challenging. State-of-the-art methods for performance modeling and analyses rely on predictive machine learning models, therefore, they become (i) unreliable in unseen environments (e.g., different hardware, workloads), and (ii) may produce incorrect explanations. To tackle this, we propose a new method, called Unicorn, which (i) captures intricate interactions between configuration options across the software-hardware stack and (ii) describes how such interactions can impact performance variations via causal inference. We evaluated Unicorn on six highly configurable systems, including three on-device machine learning systems, a video encoder, a database management system, and a data analytics pipeline. The experimental results indicate that Unicorn outperforms state-of-the-art performance debugging and optimization methods in finding effective repairs for performance faults and finding configurations with near-optimal performance. Further, unlike the existing methods, the learned causal performance models reliably predict performance for new environments.